Alfalfa is planted in more than 30 million hectares worldwide, but despite its popularity in temperate regions, it is not widely grown in subtropical agroecosystems. It is critical to improve alfalfa for such regions, considering current predictions of global warming and the increasing demands for animal-based products. In this study, we examined the diversity present in subtropical alfalfa germplasm and reported genetic parameters for forage production. An initial screening was performed from 2014 to 2016, evaluating 121 populations from different subtropical origins. Then, a breeding population was created by crossing selected plants, resulting in 145 full-sib and 36 half-sib families, which were planted in a row-column design with augmented representation of three controls (‘Bulldog805′, ‘FL99′ and ‘UF2015′). Dry matter yield (DMY), canopy height (AH), and percentage blooming (BLOOM) were measured across several harvests. Moderate narrow-sense heritability and high genetic correlations between consecutive harvests were estimated for all traits. The breeding line UF2015 produced higher DMY than FL99 and Bulldog805, and it could be a candidate cultivar release. Several families produced higher DMY than all checks, and they can be utilized to develop high yielding and adapted alfalfa cultivars for subtropical agroecosystems.

The use of imagery collected from small unmanned aerial vehicles (UAVs) in turfgrass breeding has rapidly increased, as has the demand to develop drought‐resistant cultivars. However, prior to adopting UAVs to help guide turfgrass selection under drought stress conditions, a clear understanding of the value and predictive ability of imagery‐based turfgrass characterization is required. In St. Augustinegrass, a major warm‐season turfgrass species grown in the Southeastern United States, limited research has been published about characterizing drought stress using aerial imagery. Specifically, no efforts have compared the various vegetation indices (VIs) commonly used to evaluate vegetative health in other species and sought to identify the most useful index for phenotyping drought stress traits in St. Augustinegrass. In this study, traditional ground‐based approaches for measuring percent green cover (PGC) and normalized difference vegetation index (NDVI) were compared against their UAV‐derived counterparts as well as 13 VIs under drought and non‐drought conditions, and broad‐sense heritability (H²) was calculated. A population of 115 genotypes from a ‘‘Raleigh’’ × ‘‘Seville’’ cross were analyzed at two environmentally distinct field sites in North Carolina. At both sites, a significant relationship between ground‐based and UAV‐derived measurements for PGC and NDVI was observed before and during drought (r = 0.82 to 0.95) and suggests a clear advantage to using UAVs for phenotyping drought traits given the reduced time and labor costs compared to on‐ground efforts. Among all VIs compared, UAV‐derived NDVI (NDVI‐U) showed strong correlation with the PGC taken on the ground (r > 0.85), a similar trend over time, and a higher H² estimate under drought conditions, suggesting that NDVI‐U has the potential to assist in the selection of St. Augustinegrass genotypes with the best phenotypic response to drought. Implementing UAV imagery‐based high‐throughput methods will allow breeders to evaluate germplasm with unbiased quantitative consistency, quickly and thoroughly, and with increased frequency—all without sacrificing the response to selection potential.

In turfgrass breeding, drought resistance is a primary trait for improvement due to scarcity and reduced quality of water for irrigation. Therefore, in 2010, the turfgrass breeding programs at six public universities joined efforts to address these challenges by cross evaluating breeding lines for the most economically significant warm‐season turfgrass species in the southern United States through a United States Department of Agriculture‐National Institute for Food and Agriculture Specialty Crop Research Initiative funded project. Three breeding cycles were associated with three completed (2010–2014, 2014–2019, and 2019–2024) collaborative grant projects, but the efficiency of this partnership in terms of gains from selection has not been measured. Our objectives were to (1) estimate the expected and realized genetic gain for drought resistance and turfgrass quality for three breeding cycles, (2) compare cultivars developed with support of the projects versus standard cultivars in a historical data analysis, and (3) compare genetic gain for traits assessed visually versus using small unmanned aircraft systems imagery, both in drought and non‐drought environments. For these purposes, historical data were investigated with a retrospective analysis of project trials evaluated 2011–2024. Our findings for the realized genetic gain demonstrated progress in enhancing drought resistance in bermudagrass, St. Augustinegrass, seashore paspalum, and zoysiagrass. In addition, notable positive increments for this trait were documented for each cycle compared to the standard cultivars, particularly in bermudagrass, St. Augustinegrass, and zoysiagrass. While heritability was higher for visually assessed traits, genetic increments were more pronounced for imagery‐assessed traits.